@inproceedings{li-etal-2026-dutirshlee,
title = "dutirshlee at {S}em{E}val-2026 Task 11: Symbolic Augmentation for Content-Bias-Resistant Syllogistic Reasoning",
author = "Li, Songhuan and
Yang, Liang and
Yin, Shengdi and
Zhang, Qiang and
Lin, Hongfei",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.73/",
pages = "509--513",
ISBN = "979-8-89176-414-9",
abstract = "We describe our system for SemEval-2026 Task 11 Subtask 1 (English syllogistic validity). Our approach fine-tunes Qwen2.5-7B-Instruct with LoRA and a symbolic data augmentation (SDA) scheme that replaces real-world entities with abstract placeholders, explicitly decoupling logical form from content. The resulting model achieves 96.34{\%} accuracy and a total content effect (TCE) of 2.15, yielding a primary score of 44.86. We provide detailed ablations and negative results (prompting, self consistency, contrastive decoding, structured chain-of-thought, andDPO)tocharacterizewhy direct LoRA training with SDA is the most ro bust configuration for this task. Finally, we use a specialist{--}generalist complementarity setting where a strong API model (ACC 99.48, TCE 1.06, score 57.68) is corrected by the SDA spe cialist on a single disagreement, producing a merged output with ACC 100 and TCE 0."
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<abstract>We describe our system for SemEval-2026 Task 11 Subtask 1 (English syllogistic validity). Our approach fine-tunes Qwen2.5-7B-Instruct with LoRA and a symbolic data augmentation (SDA) scheme that replaces real-world entities with abstract placeholders, explicitly decoupling logical form from content. The resulting model achieves 96.34% accuracy and a total content effect (TCE) of 2.15, yielding a primary score of 44.86. We provide detailed ablations and negative results (prompting, self consistency, contrastive decoding, structured chain-of-thought, andDPO)tocharacterizewhy direct LoRA training with SDA is the most ro bust configuration for this task. Finally, we use a specialist–generalist complementarity setting where a strong API model (ACC 99.48, TCE 1.06, score 57.68) is corrected by the SDA spe cialist on a single disagreement, producing a merged output with ACC 100 and TCE 0.</abstract>
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%0 Conference Proceedings
%T dutirshlee at SemEval-2026 Task 11: Symbolic Augmentation for Content-Bias-Resistant Syllogistic Reasoning
%A Li, Songhuan
%A Yang, Liang
%A Yin, Shengdi
%A Zhang, Qiang
%A Lin, Hongfei
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F li-etal-2026-dutirshlee
%X We describe our system for SemEval-2026 Task 11 Subtask 1 (English syllogistic validity). Our approach fine-tunes Qwen2.5-7B-Instruct with LoRA and a symbolic data augmentation (SDA) scheme that replaces real-world entities with abstract placeholders, explicitly decoupling logical form from content. The resulting model achieves 96.34% accuracy and a total content effect (TCE) of 2.15, yielding a primary score of 44.86. We provide detailed ablations and negative results (prompting, self consistency, contrastive decoding, structured chain-of-thought, andDPO)tocharacterizewhy direct LoRA training with SDA is the most ro bust configuration for this task. Finally, we use a specialist–generalist complementarity setting where a strong API model (ACC 99.48, TCE 1.06, score 57.68) is corrected by the SDA spe cialist on a single disagreement, producing a merged output with ACC 100 and TCE 0.
%U https://aclanthology.org/2026.semeval-1.73/
%P 509-513
Markdown (Informal)
[dutirshlee at SemEval-2026 Task 11: Symbolic Augmentation for Content-Bias-Resistant Syllogistic Reasoning](https://aclanthology.org/2026.semeval-1.73/) (Li et al., SemEval 2026)
ACL